diff --git "a/Data Analitics/Week 4/TU257-Lab3-1-DataExploration.ipynb" "b/Data Analitics/Week 4/TU257-Lab3-1-DataExploration.ipynb"
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+++ "b/Data Analitics/Week 4/TU257-Lab3-1-DataExploration.ipynb"
@@ -0,0 +1,1853 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GgzCNBxVUuGo"
+   },
+   "source": [
+    "# Lab 2 - Sample solution\n",
+    "\n",
+    "This jupyter notebook contains a sample solution for Week 03 - Lab 02.\n",
+    "\n",
+    "To make sure you can run the entire notebook, check if you have the required libraries installed:\n",
+    "\n",
+    "```{pytnon}\n",
+    "pip install -U pandas matplotlib seaborn\n",
+    "```\n",
+    "\n",
+    "We first start by loading the required libraries. For this particular solution we need to load the `pandas` library:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "IqTcF3XBT3t0"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "75T1IiwjVFpb"
+   },
+   "source": [
+    "Now that we have the module loaded, let's read the csv dataset. To do that, we use the [`read_csv`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html) function provided by the module. Note that we need to set the `sep` parameter to `','` given that is used to structure the file.\n",
+    "\n",
+    "***Tip***: whenever possible, check the csv file before trying to read it to check for  the file structure and the types of separator is used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "WxkHdhCQUF15"
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('C:\\\\Users\\\\Rafael\\\\Documents\\\\DataScience\\\\Data Analitics\\\\Week4\\\\trainingset.csv', sep=',')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "OSYYGia0V1JD"
+   },
+   "source": [
+    "Now, let's inspect the first few lines of the dataset to see what the data looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 27272 entries, 0 to 27271\n",
+      "Data columns (total 17 columns):\n",
+      " #   Column     Non-Null Count  Dtype  \n",
+      "---  ------     --------------  -----  \n",
+      " 0   age        24570 non-null  float64\n",
+      " 1   job        24314 non-null  object \n",
+      " 2   marital    22849 non-null  object \n",
+      " 3   education  20647 non-null  object \n",
+      " 4   default    23798 non-null  object \n",
+      " 5   balance    22510 non-null  float64\n",
+      " 6   housing    25830 non-null  object \n",
+      " 7   loan       25247 non-null  object \n",
+      " 8   contact    23762 non-null  object \n",
+      " 9   day        24364 non-null  float64\n",
+      " 10  month      25496 non-null  object \n",
+      " 11  duration   23545 non-null  float64\n",
+      " 12  campaign   22815 non-null  float64\n",
+      " 13  pdays      24138 non-null  float64\n",
+      " 14  previous   24875 non-null  float64\n",
+      " 15  poutcome   26817 non-null  object \n",
+      " 16  y          27272 non-null  object \n",
+      "dtypes: float64(7), object(10)\n",
+      "memory usage: 3.5+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "id": "YbupMzsyVg5h",
+    "outputId": "f0175657-af5f-4169-ac69-87c72bce8da7"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>job</th>\n",
+       "      <th>marital</th>\n",
+       "      <th>education</th>\n",
+       "      <th>default</th>\n",
+       "      <th>balance</th>\n",
+       "      <th>housing</th>\n",
+       "      <th>loan</th>\n",
+       "      <th>contact</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>campaign</th>\n",
+       "      <th>pdays</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>poutcome</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>44.0</td>\n",
+       "      <td>JobCat9</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>no</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>may</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>TypeA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>47.0</td>\n",
+       "      <td>JobCat3</td>\n",
+       "      <td>married</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>no</td>\n",
+       "      <td>1506.0</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>may</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>TypeA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>single</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>no</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>may</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>TypeA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>26.0</td>\n",
+       "      <td>JobCat6</td>\n",
+       "      <td>single</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>no</td>\n",
+       "      <td>447.0</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>may</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>TypeA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>42.0</td>\n",
+       "      <td>JobCat4</td>\n",
+       "      <td>divorced</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>may</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>TypeA</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    age      job   marital  education default  balance housing loan  contact  \\\n",
+       "0  44.0  JobCat9       NaN  secondary      no     29.0     yes   no  unknown   \n",
+       "1  47.0  JobCat3   married    unknown      no   1506.0     yes  NaN  unknown   \n",
+       "2  31.0  unknown    single    unknown      no      1.0      no   no  unknown   \n",
+       "3  26.0  JobCat6    single   tertiary      no    447.0     yes  yes  unknown   \n",
+       "4  42.0  JobCat4  divorced   tertiary     NaN      2.0     yes   no  unknown   \n",
+       "\n",
+       "   day month  duration  campaign  pdays  previous poutcome      y  \n",
+       "0  5.0   may       0.0       1.0   -1.0       0.0  unknown  TypeA  \n",
+       "1  5.0   may       0.0       1.0   -1.0       0.0  unknown  TypeA  \n",
+       "2  5.0   may       0.0       1.0   -1.0       0.0  unknown  TypeA  \n",
+       "3  5.0   may       0.0       1.0   -1.0       0.0  unknown  TypeA  \n",
+       "4  5.0   may       NaN       1.0   -1.0       0.0  unknown  TypeA  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FLarE8DEWXg8"
+   },
+   "source": [
+    "## Data quality report\n",
+    "\n",
+    "Now we start building the data quality report for the dataset provided.\n",
+    "\n",
+    "`pandas` has a nice built-in function to generate such reports: the [`describe`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.describe.html) function. You have to call it using the same variable you stored the dataset after reading:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 297
+    },
+    "id": "TXUk4YSkW112",
+    "outputId": "91da6113-f191-4d71-ae23-18e2d0076fb2"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>balance</th>\n",
+       "      <th>day</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>campaign</th>\n",
+       "      <th>pdays</th>\n",
+       "      <th>previous</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>24570.000000</td>\n",
+       "      <td>22510.000000</td>\n",
+       "      <td>24364.000000</td>\n",
+       "      <td>23545.0</td>\n",
+       "      <td>22815.000000</td>\n",
+       "      <td>24138.000000</td>\n",
+       "      <td>24875.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>39.894302</td>\n",
+       "      <td>1373.054687</td>\n",
+       "      <td>15.779059</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.744773</td>\n",
+       "      <td>39.980943</td>\n",
+       "      <td>0.578251</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>11.445373</td>\n",
+       "      <td>3100.869406</td>\n",
+       "      <td>8.295287</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.018258</td>\n",
+       "      <td>100.027931</td>\n",
+       "      <td>1.943030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>16.000000</td>\n",
+       "      <td>-6847.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>71.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>443.000000</td>\n",
+       "      <td>16.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>48.000000</td>\n",
+       "      <td>1423.000000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>93.000000</td>\n",
+       "      <td>98417.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>63.000000</td>\n",
+       "      <td>871.000000</td>\n",
+       "      <td>58.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                age       balance           day  duration      campaign  \\\n",
+       "count  24570.000000  22510.000000  24364.000000   23545.0  22815.000000   \n",
+       "mean      39.894302   1373.054687     15.779059       0.0      2.744773   \n",
+       "std       11.445373   3100.869406      8.295287       0.0      3.018258   \n",
+       "min       16.000000  -6847.000000      1.000000       0.0      1.000000   \n",
+       "25%       31.000000     71.000000      8.000000       0.0      1.000000   \n",
+       "50%       37.000000    443.000000     16.000000       0.0      2.000000   \n",
+       "75%       48.000000   1423.000000     21.000000       0.0      3.000000   \n",
+       "max       93.000000  98417.000000     31.000000       0.0     63.000000   \n",
+       "\n",
+       "              pdays      previous  \n",
+       "count  24138.000000  24875.000000  \n",
+       "mean      39.980943      0.578251  \n",
+       "std      100.027931      1.943030  \n",
+       "min       -1.000000      0.000000  \n",
+       "25%       -1.000000      0.000000  \n",
+       "50%       -1.000000      0.000000  \n",
+       "75%       -1.000000      0.000000  \n",
+       "max      871.000000     58.000000  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Cgw1URu_W2mx"
+   },
+   "source": [
+    "Notice 2 things about the output: \n",
+    "\n",
+    "1.   The generated report have the features on the columns and the statistics on the rows. When you have lots of features, that can make the visulization and comparison of the statistics difficult.\n",
+    "2.   The generated report only contains the continuous features.\n",
+    "\n",
+    "\n",
+    "Let's first fix the position of features and the statistics. To do that, we must add a new function call after the `describe` function: the `transpose` function. This second function call will transpose the rows and columns of the report so it will much easier to visualize the statistics when we have dozen of features in our dataset.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 266
+    },
+    "id": "gGFn6AGZVhtX",
+    "outputId": "d19c1ea6-9b23-4f19-9b99-a983018a6fa6"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <td>24570.0</td>\n",
+       "      <td>39.894302</td>\n",
+       "      <td>11.445373</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>93.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>balance</th>\n",
+       "      <td>22510.0</td>\n",
+       "      <td>1373.054687</td>\n",
+       "      <td>3100.869406</td>\n",
+       "      <td>-6847.0</td>\n",
+       "      <td>71.0</td>\n",
+       "      <td>443.0</td>\n",
+       "      <td>1423.0</td>\n",
+       "      <td>98417.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>day</th>\n",
+       "      <td>24364.0</td>\n",
+       "      <td>15.779059</td>\n",
+       "      <td>8.295287</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>duration</th>\n",
+       "      <td>23545.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>campaign</th>\n",
+       "      <td>22815.0</td>\n",
+       "      <td>2.744773</td>\n",
+       "      <td>3.018258</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>63.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pdays</th>\n",
+       "      <td>24138.0</td>\n",
+       "      <td>39.980943</td>\n",
+       "      <td>100.027931</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>871.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>previous</th>\n",
+       "      <td>24875.0</td>\n",
+       "      <td>0.578251</td>\n",
+       "      <td>1.943030</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>58.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            count         mean          std     min   25%    50%     75%  \\\n",
+       "age       24570.0    39.894302    11.445373    16.0  31.0   37.0    48.0   \n",
+       "balance   22510.0  1373.054687  3100.869406 -6847.0  71.0  443.0  1423.0   \n",
+       "day       24364.0    15.779059     8.295287     1.0   8.0   16.0    21.0   \n",
+       "duration  23545.0     0.000000     0.000000     0.0   0.0    0.0     0.0   \n",
+       "campaign  22815.0     2.744773     3.018258     1.0   1.0    2.0     3.0   \n",
+       "pdays     24138.0    39.980943   100.027931    -1.0  -1.0   -1.0    -1.0   \n",
+       "previous  24875.0     0.578251     1.943030     0.0   0.0    0.0     0.0   \n",
+       "\n",
+       "              max  \n",
+       "age          93.0  \n",
+       "balance   98417.0  \n",
+       "day          31.0  \n",
+       "duration      0.0  \n",
+       "campaign     63.0  \n",
+       "pdays       871.0  \n",
+       "previous     58.0  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe().transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "SkWmJsrXYL5P"
+   },
+   "source": [
+    "Notice how it looks much nicer now.\n",
+    "\n",
+    "Let's now fix the categorical features. By default, `pandas` runs the describe function for numerical values given that the rport for continuous and categorical features are different.\n",
+    "\n",
+    "To generate a report for categorical features, we must tell the `describe`function what types of features we want to include in the report, we have to pass the parameter `include` to the function and inform which fatures we want. In `pandas`, categorical features are defined either as python `objects` or pandas `category` type. Let's generate our report for the categorical features and transpose the columns of the report at the same time for an easier visualization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 359
+    },
+    "id": "b-tnZbztWTkd",
+    "outputId": "a07191ee-474a-4aa2-fb4e-1007dffaf7c3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>unique</th>\n",
+       "      <th>top</th>\n",
+       "      <th>freq</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>job</th>\n",
+       "      <td>24314</td>\n",
+       "      <td>12</td>\n",
+       "      <td>JobCat3</td>\n",
+       "      <td>5298</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>marital</th>\n",
+       "      <td>22849</td>\n",
+       "      <td>3</td>\n",
+       "      <td>married</td>\n",
+       "      <td>13782</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>education</th>\n",
+       "      <td>20647</td>\n",
+       "      <td>4</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>10573</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>default</th>\n",
+       "      <td>23798</td>\n",
+       "      <td>2</td>\n",
+       "      <td>no</td>\n",
+       "      <td>23368</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>housing</th>\n",
+       "      <td>25830</td>\n",
+       "      <td>2</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>14336</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>loan</th>\n",
+       "      <td>25247</td>\n",
+       "      <td>2</td>\n",
+       "      <td>no</td>\n",
+       "      <td>21228</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>contact</th>\n",
+       "      <td>23762</td>\n",
+       "      <td>3</td>\n",
+       "      <td>cellular</td>\n",
+       "      <td>15300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>month</th>\n",
+       "      <td>25496</td>\n",
+       "      <td>12</td>\n",
+       "      <td>may</td>\n",
+       "      <td>7758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>poutcome</th>\n",
+       "      <td>26817</td>\n",
+       "      <td>4</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>21951</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>y</th>\n",
+       "      <td>27272</td>\n",
+       "      <td>2</td>\n",
+       "      <td>TypeA</td>\n",
+       "      <td>24030</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           count unique        top   freq\n",
+       "job        24314     12    JobCat3   5298\n",
+       "marital    22849      3    married  13782\n",
+       "education  20647      4  secondary  10573\n",
+       "default    23798      2         no  23368\n",
+       "housing    25830      2        yes  14336\n",
+       "loan       25247      2         no  21228\n",
+       "contact    23762      3   cellular  15300\n",
+       "month      25496     12        may   7758\n",
+       "poutcome   26817      4    unknown  21951\n",
+       "y          27272      2      TypeA  24030"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe(include=['object', 'category']).transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rZnajWixagCE"
+   },
+   "source": [
+    "## Visualizations\n",
+    "\n",
+    "Now that we generated the data quality report, let's generate the visualizations for the distributions of each feature.\n",
+    "\n",
+    "For that, we will use another `pandas`' built-in function: the `plot` function. When calling that function, we must pass the parameter `kind` to inform what type of plot we wish to display.\n",
+    "\n",
+    "In addition, as we are ploting individual features, we must first select the feature we want to visualize.\n",
+    "\n",
+    "***Tip***: set the `title` parameter of the plot function to make it easier to find particular plots in your notebook.\n",
+    "\n",
+    "\n",
+    ">  #### Continuous features - histograms \n",
+    "> For visualizing continuous features, we use histograms. To inform pandas to generate an histogram, we set the parameter `kind='hist'`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 298
+    },
+    "id": "TOAS9i_ZafMz",
+    "outputId": "378781be-269c-4820-aae0-fc9070fb0979"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'age'}, ylabel='Frequency'>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['age'].plot(kind='hist', title='age')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 298
+    },
+    "id": "sFKVko2AbwkP",
+    "outputId": "72a6fe2e-6f26-478d-9ed4-0631d3d45b2b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'balance'}, ylabel='Frequency'>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['balance'].plot(kind='hist', title='balance')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 298
+    },
+    "id": "PTbbd6FGbwfU",
+    "outputId": "53582be4-c5b9-4829-e371-8428d1acc36e"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'day'}, ylabel='Frequency'>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['day'].plot(kind='hist', title='day')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 298
+    },
+    "id": "PJieyquSbwcW",
+    "outputId": "813ac5e9-6e23-48ba-9a61-04fe9bb51aea"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'duration'}, ylabel='Frequency'>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['duration'].plot(kind='hist', title='duration')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 298
+    },
+    "id": "QCZJbZlKbwY8",
+    "outputId": "b8f5c54a-be95-4d84-ed1c-741f8a24e0ec"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'campaign'}, ylabel='Frequency'>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['campaign'].plot(kind='hist', title='campaign')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 298
+    },
+    "id": "HmAUUY-TbwTn",
+    "outputId": "844ef635-ff6f-497f-d1a3-8e132deae0b7"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'pdays'}, ylabel='Frequency'>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['pdays'].plot(kind='hist', title='pdays')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 298
+    },
+    "id": "l8Gkq6yAbwN6",
+    "outputId": "a1326e6e-a48f-4ce6-bb5e-6ad1cf2c5ad3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'previous'}, ylabel='Frequency'>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['previous'].plot(kind='hist',  title='previous')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lapa4Ff3cg71"
+   },
+   "source": [
+    ">  #### Categorical features - bar plots \n",
+    "> For visualizing categorical features, we use bar plots. To inform pandas to generate a bar plot, we set the parameter `kind='bar'`. However, that is not enough for the `plot` function as it doesn't know how to plot categories. To be able to generate such plots, we must generate the counts (frequency) of each category and provide those counts to the plot function. Fortunately, there is a buil-in function in pandas that does exactly that for us: `value_counts`.\n",
+    "> \n",
+    "> To use it, we first select the feature we want to plot and then we call the `value_counts` function. Finally, we call the plot function, cahining all the three together."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 334
+    },
+    "id": "HOYb7FkEbwLG",
+    "outputId": "fe375a5e-87e7-46a0-a1c8-fdf3ceb63e1c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'job'}, xlabel='job'>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['job'].value_counts().plot(kind='bar', title='job')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 331
+    },
+    "id": "bdAqKKYabwId",
+    "outputId": "35caff15-209b-429a-e145-bbd380525cd1"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'marital'}, xlabel='marital'>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['marital'].value_counts().plot(kind='bar',  title='marital')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 340
+    },
+    "id": "WXQLLabdbwFw",
+    "outputId": "7d6672fe-2bee-4e8f-9ee7-379710a30c17"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'education'}, xlabel='education'>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['education'].value_counts().plot(kind='bar',  title='education')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 306
+    },
+    "id": "-ZUMO3v8bwCV",
+    "outputId": "7a766a40-dc6e-4329-d3db-b195e8462351"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'default'}, xlabel='default'>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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UlKigoEAFBQUqKSmRy+Wybq+vr9fIkSNVU1Ojbdu2KT8/X2+++aZmzpzZ1FMCAACGatfUO4wYMUIjRoy45ExQUJAcDsdFb3O73Vq6dKlWrFihoUOHSpJeffVVxcTEaOPGjUpLS9OePXtUUFCgoqIiJSYmSpJefvllJSUlad++ferTp48KCwv1ySefqKysTE6nU5L0/PPPa8KECXr66acVFhbW1FMDAACGaZH36GzevFlRUVHq3bu3Jk+erMrKSuu24uJi1dXVKTU11TrmdDoVFxen7du3S5Lef/992e12K3IkacCAAbLb7T4zcXFxVuRIUlpamjwej4qLi1vitAAAQBvT5B2dbzJixAiNGTNGPXr00IEDB/TEE0/ojjvuUHFxsYKCglRRUaHAwECFh4f73C86OloVFRWSpIqKCkVFRTV67KioKJ+Z6Ohon9vDw8MVGBhozVzI4/HI4/FY16urq6/oXAEAQOvW7KEzbtw4689xcXG67bbb1KNHD61bt0733HPP197P6/XKZrNZ17/65yuZ+aq5c+dqzpw53+o8AABA29fiHy/v1q2bevToof3790uSHA6HamtrVVVV5TNXWVlp7dA4HA4dO3as0WMdP37cZ+bCnZuqqirV1dU12uk5b9asWXK73dalrKzsis8PAAC0Xi0eOidPnlRZWZm6desmSUpISFD79u21YcMGa6a8vFylpaVKTk6WJCUlJcntdmvnzp3WzI4dO+R2u31mSktLVV5ebs0UFhYqKChICQkJF11LUFCQwsLCfC4AAMBcTX7p6uzZs/rnP/9pXT9w4IBKSkoUERGhiIgIzZ49Wz/+8Y/VrVs3HTx4UL/61a8UGRmpH/3oR5Iku92uSZMmaebMmerSpYsiIiKUk5Oj+Ph461NYffv21fDhwzV58mQtWbJEkjRlyhSlp6erT58+kqTU1FT169dPLpdLzz77rE6dOqWcnBxNnjyZgAEAAJIuI3Q++OADDRkyxLo+Y8YMSdKDDz6oF198UR9//LGWL1+u06dPq1u3bhoyZIhWrVql0NBQ6z4LFy5Uu3btNHbsWJ07d0533nmn8vLyFBAQYM2sXLlSWVlZ1qezMjIyfL67JyAgQOvWrdO0adM0cOBABQcHKzMzU88991zTfwoAAMBINq/X6/X3Ivylurpadrtdbrf7mtsF6vnYOn8vAVfRwWdG+nsJANBsmvLvN7/rCgAAGIvQAQAAxiJ0AACAsQgdAABgLEIHAAAYi9ABAADGInQAAICxCB0AAGAsQgcAABiL0AEAAMYidAAAgLEIHQAAYCxCBwAAGIvQAQAAxiJ0AACAsQgdAABgLEIHAAAYi9ABAADGInQAAICxCB0AAGAsQgcAABiL0AEAAMYidAAAgLEIHQAAYCxCBwAAGIvQAQAAxiJ0AACAsQgdAABgLEIHAAAYi9ABAADGInQAAICxCB0AAGAsQgcAABiL0AEAAMYidAAAgLEIHQAAYCxCBwAAGIvQAQAAxiJ0AACAsQgdAABgLEIHAAAYi9ABAADGInQAAICxCB0AAGAsQgcAABiL0AEAAMYidAAAgLEIHQAAYCxCBwAAGIvQAQAAxiJ0AACAsQgdAABgLEIHAAAYi9ABAADGInQAAICxCB0AAGAsQgcAABiL0AEAAMYidAAAgLEIHQAAYCxCBwAAGIvQAQAAxiJ0AACAsQgdAABgLEIHAAAYq8mh895772nUqFFyOp2y2Wxau3atz+1er1ezZ8+W0+lUcHCwUlJStHv3bp8Zj8ej6dOnKzIyUiEhIcrIyNDhw4d9ZqqqquRyuWS322W32+VyuXT69GmfmUOHDmnUqFEKCQlRZGSksrKyVFtb29RTAgAAhmpy6NTU1Oimm27S4sWLL3r7/PnztWDBAi1evFi7du2Sw+HQsGHDdObMGWsmOztba9asUX5+vrZt26azZ88qPT1d9fX11kxmZqZKSkpUUFCggoIClZSUyOVyWbfX19dr5MiRqqmp0bZt25Sfn68333xTM2fObOopAQAAQ9m8Xq/3su9ss2nNmjW6++67JX25m+N0OpWdna1f/vKXkr7cvYmOjta8efM0depUud1ude3aVStWrNC4ceMkSUePHlVMTIzWr1+vtLQ07dmzR/369VNRUZESExMlSUVFRUpKStLevXvVp08fvf3220pPT1dZWZmcTqckKT8/XxMmTFBlZaXCwsK+cf3V1dWy2+1yu93fat4kPR9b5+8l4Co6+MxIfy8BAJpNU/79btb36Bw4cEAVFRVKTU21jgUFBWnw4MHavn27JKm4uFh1dXU+M06nU3FxcdbM+++/L7vdbkWOJA0YMEB2u91nJi4uzoocSUpLS5PH41FxcfFF1+fxeFRdXe1zAQAA5mrW0KmoqJAkRUdH+xyPjo62bquoqFBgYKDCw8MvORMVFdXo8aOionxmLnye8PBwBQYGWjMXmjt3rvWeH7vdrpiYmMs4SwAA0Fa0yKeubDabz3Wv19vo2IUunLnY/OXMfNWsWbPkdrutS1lZ2SXXBAAA2rZmDR2HwyFJjXZUKisrrd0Xh8Oh2tpaVVVVXXLm2LFjjR7/+PHjPjMXPk9VVZXq6uoa7fScFxQUpLCwMJ8LAAAwV7OGTmxsrBwOhzZs2GAdq62t1ZYtW5ScnCxJSkhIUPv27X1mysvLVVpaas0kJSXJ7XZr586d1syOHTvkdrt9ZkpLS1VeXm7NFBYWKigoSAkJCc15WgAAoI1q19Q7nD17Vv/85z+t6wcOHFBJSYkiIiJ0/fXXKzs7W7m5uerVq5d69eql3NxcdezYUZmZmZIku92uSZMmaebMmerSpYsiIiKUk5Oj+Ph4DR06VJLUt29fDR8+XJMnT9aSJUskSVOmTFF6err69OkjSUpNTVW/fv3kcrn07LPP6tSpU8rJydHkyZPZqQEAAJIuI3Q++OADDRkyxLo+Y8YMSdKDDz6ovLw8Pfroozp37pymTZumqqoqJSYmqrCwUKGhodZ9Fi5cqHbt2mns2LE6d+6c7rzzTuXl5SkgIMCaWblypbKysqxPZ2VkZPh8d09AQIDWrVunadOmaeDAgQoODlZmZqaee+65pv8UAACAka7oe3TaOr5HB9cKvkcHgEn89j06AAAArQmhAwAAjEXoAAAAYxE6AADAWIQOAAAwFqEDAACMRegAAABjEToAAMBYhA4AADAWoQMAAIxF6AAAAGMROgAAwFiEDgAAMBahAwAAjEXoAAAAYxE6AADAWIQOAAAwFqEDAACMRegAAABjEToAAMBYhA4AADAWoQMAAIxF6AAAAGMROgAAwFiEDgAAMBahAwAAjEXoAAAAYxE6AADAWIQOAAAwFqEDAACMRegAAABjEToAAMBYhA4AADAWoQMAAIxF6AAAAGMROgAAwFiEDgAAMBahAwAAjEXoAAAAYxE6AADAWIQOAAAwFqEDAACMRegAAABjEToAAMBYhA4AADAWoQMAAIxF6AAAAGMROgAAwFiEDgAAMBahAwAAjEXoAAAAYxE6AADAWIQOAAAwFqEDAACMRegAAABjEToAAMBYhA4AADAWoQMAAIxF6AAAAGMROgAAwFiEDgAAMBahAwAAjEXoAAAAYxE6AADAWM0eOrNnz5bNZvO5OBwO63av16vZs2fL6XQqODhYKSkp2r17t89jeDweTZ8+XZGRkQoJCVFGRoYOHz7sM1NVVSWXyyW73S673S6Xy6XTp0839+kAAIA2rEV2dL73ve+pvLzcunz88cfWbfPnz9eCBQu0ePFi7dq1Sw6HQ8OGDdOZM2esmezsbK1Zs0b5+fnatm2bzp49q/T0dNXX11szmZmZKikpUUFBgQoKClRSUiKXy9USpwMAANqodi3yoO3a+ezinOf1erVo0SI9/vjjuueeeyRJy5YtU3R0tF577TVNnTpVbrdbS5cu1YoVKzR06FBJ0quvvqqYmBht3LhRaWlp2rNnjwoKClRUVKTExERJ0ssvv6ykpCTt27dPffr0aYnTAgAAbUyL7Ojs379fTqdTsbGxuvfee/XZZ59Jkg4cOKCKigqlpqZas0FBQRo8eLC2b98uSSouLlZdXZ3PjNPpVFxcnDXz/vvvy263W5EjSQMGDJDdbrdmAAAAmn1HJzExUcuXL1fv3r117NgxPfXUU0pOTtbu3btVUVEhSYqOjva5T3R0tD7//HNJUkVFhQIDAxUeHt5o5vz9KyoqFBUV1ei5o6KirJmL8Xg88ng81vXq6urLO0kAANAmNHvojBgxwvpzfHy8kpKSdMMNN2jZsmUaMGCAJMlms/ncx+v1Njp2oQtnLjb/TY8zd+5czZkz51udBwAAaPta/OPlISEhio+P1/79+6337Vy461JZWWnt8jgcDtXW1qqqquqSM8eOHWv0XMePH2+0W/RVs2bNktvtti5lZWVXdG4AAKB1a/HQ8Xg82rNnj7p166bY2Fg5HA5t2LDBur22tlZbtmxRcnKyJCkhIUHt27f3mSkvL1dpaak1k5SUJLfbrZ07d1ozO3bskNvttmYuJigoSGFhYT4XAABgrmZ/6SonJ0ejRo3S9ddfr8rKSj311FOqrq7Wgw8+KJvNpuzsbOXm5qpXr17q1auXcnNz1bFjR2VmZkqS7Ha7Jk2apJkzZ6pLly6KiIhQTk6O4uPjrU9h9e3bV8OHD9fkyZO1ZMkSSdKUKVOUnp7OJ64AAICl2UPn8OHDuu+++3TixAl17dpVAwYMUFFRkXr06CFJevTRR3Xu3DlNmzZNVVVVSkxMVGFhoUJDQ63HWLhwodq1a6exY8fq3LlzuvPOO5WXl6eAgABrZuXKlcrKyrI+nZWRkaHFixc39+kAAIA2zOb1er3+XoS/VFdXy263y+12X3MvY/V8bJ2/l4Cr6OAzI/29BABoNk3595vfdQUAAIxF6AAAAGMROgAAwFiEDgAAMBahAwAAjEXoAAAAYxE6AADAWIQOAAAwFqEDAACMRegAAABjEToAAMBYhA4AADAWoQMAAIxF6AAAAGMROgAAwFiEDgAAMBahAwAAjEXoAAAAYxE6AADAWIQOAAAwFqEDAACMRegAAABjEToAAMBYhA4AADAWoQMAAIxF6AAAAGMROgAAwFiEDgAAMBahAwAAjEXoAAAAYxE6AADAWIQOAAAwFqEDAACMRegAAABjEToAAMBYhA4AADAWoQMAAIxF6AAAAGMROgAAwFiEDgAAMBahAwAAjEXoAAAAYxE6AADAWIQOAAAwFqEDAACMRegAAABjEToAAMBYhA4AADAWoQMAAIxF6AAAAGMROgAAwFiEDgAAMBahAwAAjEXoAAAAYxE6AADAWIQOAAAwFqEDAACMRegAAABjEToAAMBYhA4AADAWoQMAAIxF6AAAAGO18/cCAADNq+dj6/y9BFxFB58Z6e8ltGrs6AAAAGO1+dD5/e9/r9jYWHXo0EEJCQnaunWrv5cEAABaiTYdOqtWrVJ2drYef/xx/eMf/9Dtt9+uESNG6NChQ/5eGgAAaAXadOgsWLBAkyZN0sMPP6y+fftq0aJFiomJ0YsvvujvpQEAgFagzYZObW2tiouLlZqa6nM8NTVV27dv99OqAABAa9JmP3V14sQJ1dfXKzo62ud4dHS0KioqLnofj8cjj8djXXe73ZKk6urqlltoK9Xg+be/l4Cr6Fr8b/xaxt/va8u1+Pf7/Dl7vd5vnG2zoXOezWbzue71ehsdO2/u3LmaM2dOo+MxMTEtsjagtbAv8vcKALSUa/nv95kzZ2S32y8502ZDJzIyUgEBAY12byorKxvt8pw3a9YszZgxw7re0NCgU6dOqUuXLl8bRzBHdXW1YmJiVFZWprCwMH8vB0Az4u/3tcXr9erMmTNyOp3fONtmQycwMFAJCQnasGGDfvSjH1nHN2zYoNGjR1/0PkFBQQoKCvI51rlz55ZcJlqhsLAw/kcIGIq/39eOb9rJOa/Nho4kzZgxQy6XS7fddpuSkpL00ksv6dChQ/rpT3/q76UBAIBWoE2Hzrhx43Ty5En99re/VXl5ueLi4rR+/Xr16NHD30sDAACtQJsOHUmaNm2apk2b5u9loA0ICgrSk08+2ejlSwBtH3+/8XVs3m/z2SwAAIA2qM1+YSAAAMA3IXQAAICxCB0AAGAsQgcAABiL0AEAAMYidAAARqivr1dJSYmqqqr8vRS0IoQOrgnFxcV69dVXtXLlSv3973/393IANIPs7GwtXbpU0peRM3jwYN16662KiYnR5s2b/bs4tBpt/gsDgUuprKzUvffeq82bN6tz587yer1yu90aMmSI8vPz1bVrV38vEcBl+tOf/qTx48dLkv7617/qwIED2rt3r5YvX67HH39cf/vb3/y8QrQG7OjAaNOnT1d1dbV2796tU6dOqaqqSqWlpaqurlZWVpa/lwfgCpw4cUIOh0OStH79eo0ZM0a9e/fWpEmT9PHHH/t5dWgtCB0YraCgQC+++KL69u1rHevXr59eeOEFvf32235cGYArFR0drU8++UT19fUqKCjQ0KFDJUn//ve/FRAQ4OfVobXgpSsYraGhQe3bt290vH379mpoaPDDigA0l4ceekhjx45Vt27dZLPZNGzYMEnSjh079F//9V9+Xh1aC37XFYw2evRonT59Wq+//rqcTqck6ciRI7r//vsVHh6uNWvW+HmFAK7En/70J5WVlWnMmDHq3r27JGnZsmXq3LmzRo8e7efVoTUgdGC0srIyjR49WqWlpYqJiZHNZtPnn3+u/v37a+3atYqJifH3EgE0gy+++EIdOnTw9zLQChE6uCZs3LhRe/bskdfrVb9+/azX8gG0XfX19crNzdUf/vAHHTt2TJ9++qm++93v6oknnlDPnj01adIkfy8RrQBvRobx3nnnHW3atEkffvihSkpK9Nprr2nixImaOHGiv5cG4Ao8/fTTysvL0/z58xUYGGgdj4+P1x//+Ec/rgytCaEDo82ZM0epqal65513dOLECVVVVflcALRdy5cv10svvaT777/f51NW/fv31969e/24MrQmfOoKRvvDH/6gvLw8uVwufy8FQDM7cuSIbrzxxkbHGxoaVFdX54cVoTViRwdGq62tVXJysr+XAaAFfO9739PWrVsbHX/jjTd0yy23+GFFaI3Y0YHRHn74Yb322mt64okn/L0UAM3sySeflMvl0pEjR9TQ0KDVq1dr3759Wr58ud566y1/Lw+tBJ+6gtH++7//W8uXL1f//v3Vv3//Rl8euGDBAj+tDEBz+L//+z/l5uaquLhYDQ0NuvXWW/Wb3/xGqamp/l4aWglCB0YbMmTI195ms9m0adOmq7gaAM1pwoQJmjhxogYNGuTvpaAV46UrGO3dd9/19xIAtJAzZ84oNTVVMTExeuihhzRhwgTrG9CB83gzMgCgTXrzzTd15MgRPfLII3rjjTfUo0cPjRgxQm+88QafuoKFl64AAEb4xz/+of/93//VH//4R3Xq1Enjx4/XtGnT1KtXL38vDX7Ejg4AoM0rLy9XYWGhCgsLFRAQoLvuuku7d+9Wv379tHDhQn8vD37Ejg4AoE2qq6vTX/7yF73yyisqLCxU//799fDDD+v+++9XaGioJCk/P18/+9nP+Cb0axhvRgYAtEndunVTQ0OD7rvvPu3cuVM333xzo5m0tDR17tz5qq8NrQc7OgCANmnFihUaM2aMOnTo4O+loBUjdAAAgLF4MzIAADAWoQMAAIxF6AAAAGMROgBanZSUFGVnZ3/r+bVr1+rGG29UQEBAk+73TWw2m9auXdtsjwfg6iN0ALR5U6dO1U9+8hOVlZXpd7/7XYs8x8GDB2Wz2VRSUtIijw+gZfA9OgDatLNnz6qyslJpaWn8QkcAjbCjA8Cvampq9MADD6hTp07q1q2bnn/+eZ/ba2tr9eijj+o73/mOQkJClJiYqM2bN0uSNm/ebH0D7h133CGbzabNmzfr5MmTuu+++9S9e3d17NhR8fHxev31130et2fPnlq0aJHPsZtvvlmzZ8++6DpjY2MlSbfccotsNptSUlKu+NwBtDxCB4Bf/eIXv9C7776rNWvWqLCwUJs3b1ZxcbF1+0MPPaS//e1vys/P10cffaQxY8Zo+PDh2r9/v5KTk7Vv3z5JX/4m6/LyciUnJ+uLL75QQkKC3nrrLZWWlmrKlClyuVzasWPHZa9z586dkqSNGzeqvLxcq1evvrITB3BV8NIVAL85e/asli5dquXLl2vYsGGSpGXLlql79+6SpH/96196/fXXdfjwYetlqZycHBUUFOiVV15Rbm6uoqKiJEkRERFyOBySpO985zvKycmxnmf69OkqKCjQG2+8ocTExMtaa9euXSVJXbp0sZ4HQOtH6ADwm3/961+qra1VUlKSdSwiIkJ9+vSRJP3973+X1+tV7969fe7n8XjUpUuXr33c+vp6PfPMM1q1apWOHDkij8cjj8ejkJCQljkRAK0WoQPAb77pN9A0NDQoICBAxcXFCggI8LmtU6dOX3u/559/XgsXLtSiRYsUHx+vkJAQZWdnq7a21pq57rrrGj1/XV3dZZwFgNaM0AHgNzfeeKPat2+voqIiXX/99ZKkqqoqffrppxo8eLBuueUW1dfXq7KyUrfffvu3ftytW7dq9OjRGj9+vKQvg2n//v3q27evNdO1a1eVl5db16urq3XgwIGvfczAwEBJX+4WAWg7eDMyAL/p1KmTJk2apF/84hd65513VFpaqgkTJui66778X1Pv3r11//3364EHHtDq1at14MAB7dq1S/PmzdP69eu/9nFvvPFGbdiwQdu3b9eePXs0depUVVRU+MzccccdWrFihbZu3arS0lI9+OCDjXaNvioqKkrBwcEqKCjQsWPH5Ha7m+eHAKBFEToA/OrZZ5/VoEGDlJGRoaFDh+qHP/yhEhISrNtfeeUVPfDAA5o5c6b69OmjjIwM7dixQzExMV/7mE888YRuvfVWpaWlKSUlRQ6HQ3fffbfPzKxZszRo0CClp6frrrvu0t13360bbrjhax+zXbt2+p//+R8tWbJETqdTo0ePvuJzB9DybN5vepEcAACgjWJHBwAAGIvQAQAAxiJ0AACAsQgdAABgLEIHAAAYi9ABAADGInQAAICxCB0AAGAsQgcAABiL0AEAAMYidAAAgLEIHQAAYKz/B9gvbm69D0/dAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['default'].value_counts().plot(kind='bar', title='default')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 306
+    },
+    "id": "8ncyjAdXdxmR",
+    "outputId": "4e97b796-73b0-4500-f97e-4d9d58319324"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'housing'}, xlabel='housing'>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['housing'].value_counts().plot(kind='bar',  title='housing')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 306
+    },
+    "id": "KlMoAlVRdz8I",
+    "outputId": "c5a4f5d1-ba0c-40e3-b449-60fce01ef453"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'loan'}, xlabel='loan'>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['loan'].value_counts().plot(kind='bar', title='loan')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 338
+    },
+    "id": "Q3i-VclFd1Ca",
+    "outputId": "8125181d-ed90-42d6-ece2-a7bcf30d973f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'contact'}, xlabel='contact'>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['contact'].value_counts().plot(kind='bar',  title='contact')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 310
+    },
+    "id": "p6Y57qXrd3C2",
+    "outputId": "1f8ec7ec-0df9-4267-c329-dd2aa55e5c75"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'month'}, xlabel='month'>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['month'].value_counts().plot(kind='bar', title='month')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 334
+    },
+    "id": "3xJLvm0Ad3-G",
+    "outputId": "46e127e7-22bb-4168-c829-be95c2ceef7b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'poutcome'}, xlabel='poutcome'>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['poutcome'].value_counts().plot(kind='bar',  title='poutcome')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XFhqUr9LeWSb"
+   },
+   "source": [
+    ">  #### Target features \n",
+    "> Let's not forget the target feature. Given that it is a categorical feature, we'll use a bar plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 318
+    },
+    "id": "HEKGqnGXeiZ2",
+    "outputId": "ee815d6a-bcde-4555-9ff6-ba336257b1c6"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'target feature'}, xlabel='y'>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['y'].value_counts().plot(kind='bar',  title='target feature')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "soYbhq1_eob_"
+   },
+   "source": [
+    "## Bonus 1 - Ploting several features at once\n",
+    "\n",
+    "Let's now see how we can visualize several features at once using `pandas` and the `matplotlib` [docs](https://matplotlib.org/contents.html) modules.\n",
+    "\n",
+    "For ploting several features at once, we will create a `figure` using matplotlib built-in function `subplots`. The `figure` object will hold the invidual plots. Therefore, when we create that object, we must inform how many rows and how many columns that figure will contain. In addition, we have to define the size of the figure using the `figsize` parameter. \n",
+    "\n",
+    "This is a sample of how the code will look like:\n",
+    "\n",
+    "```{python} \n",
+    "fig, axes = plt.subplots(nrows=3,ncols=3, figsize=(18, 12))\n",
+    "```\n",
+    "\n",
+    "The `fig` variable contains the figure itself. The `axes` variables contains an object that represent the positions and a reference for each plots within the figure. The reference contained in `axes` is a coordinate system that we can reference using the syntax:\n",
+    "\n",
+    "```{python}\n",
+    "[row_index, column_index]\n",
+    "```\n",
+    "\n",
+    "***Tip***:  sometimes it can be hard to identify separations of values of features in plots generated with `matplotlib` . It is also difficult to identify the values for frequency as well. To help this out, we'll use a python module called `seaborn` [docs](https://seaborn.pydata.org/). To make the plots look a bit nicer we must import the `seaborn` module and call the built-in function `set`. That will change the default look and feel of the plots, including plots generated with `pandas`.\n",
+    "\n",
+    "***Tip 2:*** put the code for setting `seaborn` look and feel at the top of the jupyter notebook to change all `matplotlib` plots.\n",
+    "\n",
+    "***Tip 3:*** once the plots are in place, use the function `tight_layout` to fit all the plots in the image and organize the titles, labels and legends."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "id": "bjqmDjt7WH78"
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XIy_f9O3kudE"
+   },
+   "source": [
+    "### Continuous features - histograms\n",
+    "\n",
+    "There are 7 continuous features in our datasets. Let's create a figure with 3 columns to plot our features. That way, we need to create a figure with 3 rows of plots. This will leave us with 9 positions in the figure: \n",
+    "\n",
+    "$3\\text{ rows}\\times3\\text{ columns}=9 \\text{ positions}$\n",
+    "\n",
+    "Once we have the coordinates, we inform the position of each plot in the `ax` parameter of the plot `function`.\n",
+    "\n",
+    "Now that we have the figure and the reference to the axes, lets generate all plots at once. When we do that, we must tell the `plot` function to position the plot in a particular coordinate of the figure. Once we plot all features, we can proceed to remove the 'empty' plots (there will be 2 empty plots as we have 9 positions and only 7 features).\n",
+    "\n",
+    "For more details see the comments in the code below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 869
+    },
+    "id": "v-2GKH_GXCM3",
+    "outputId": "57efd60a-4b06-4c49-ee73-dbc215074241"
+   },
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 1800x1200 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# let's create the figure and axes wit 3 rows anc 3 columns\n",
+    "fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(18, 12))\n",
+    "\n",
+    "# now we create the plots, informing the coordinate\n",
+    "df['age'].plot(kind='hist', ax=axes[0, 0], title='age')  # position [0, 0]\n",
+    "df['balance'].plot(kind='hist', ax=axes[0, 1], title='balance') # position [0, 1]\n",
+    "df['day'].plot(kind='hist', ax=axes[0, 2], title='day') # position [0, 2]\n",
+    "df['duration'].plot(kind='hist', ax=axes[1, 0], title='duration') # position [1, 0]\n",
+    "df['campaign'].plot(kind='hist', ax=axes[1, 1], title='campaign') # position [1, 1]\n",
+    "df['pdays'].plot(kind='hist', ax=axes[1, 2], title='pdays') # position [1, 2]\n",
+    "df['previous'].plot(kind='hist', ax=axes[2, 0], title='previous') # position [2, 0]\n",
+    "\n",
+    "# now we remove the plots\n",
+    "fig.delaxes(axes[2, 1]) # position [2, 1]\n",
+    "fig.delaxes(axes[2, 2]) # position [2, 2]\n",
+    "\n",
+    "# here we tidy the figure layout\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "aAWZWLhvq5DC"
+   },
+   "source": [
+    "### Categorical features - bar plots\n",
+    "\n",
+    "There are 9 categorical features in our datasets. Let's create a figure with 3 columns to plot our features. That way, we need once again to create a figure with 3 rows of plots. This will leave us with 9 positions in the figure: \n",
+    "\n",
+    "$3\\text{ rows}\\times3\\text{ columns}=9 \\text{ positions}$\n",
+    "\n",
+    "There will be no empty plots in the figure. The rest of the process is the same as for categorical features. For more details see the comments in the code below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 869
+    },
+    "id": "fnKKEe8rcX4K",
+    "outputId": "4f2cb5b8-3477-4e2d-873c-d98dbf21373b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x1200 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# let's create the figure and axes wit 3 rows anc 3 columns\n",
+    "fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(18, 12))\n",
+    "\n",
+    "# now we create the plots, informing the coordinate - notice that we have to use\n",
+    "# the value_couts function because we're ploting categorical features\n",
+    "df['job'].value_counts().plot(kind='bar', ax=axes[0, 0], title='job') # position [0, 0]\n",
+    "df['marital'].value_counts().plot(kind='bar', ax=axes[0, 1], title='marital') # position [0, 1]\n",
+    "df['education'].value_counts().plot(kind='bar', ax=axes[0, 2], title='education') # position [0, 2]\n",
+    "df['default'].value_counts().plot(kind='bar', ax=axes[1, 0], title='default') # position [1, 0]\n",
+    "df['housing'].value_counts().plot(kind='bar', ax=axes[1, 1], title='housing')# position [1, 1]\n",
+    "df['loan'].value_counts().plot(kind='bar', ax=axes[1, 2], title='loan') # position [1, 2]\n",
+    "df['contact'].value_counts().plot(kind='bar', ax=axes[2, 0], title='contact') # position [2, 0]\n",
+    "df['month'].value_counts().plot(kind='bar', ax=axes[2, 1], title='month') # position [2, 1]\n",
+    "df['poutcome'].value_counts().plot(kind='bar', ax=axes[2, 2], title='poutcome') # position [2, 2]\n",
+    "\n",
+    "# here we tidy the figure layout\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 297
+    },
+    "id": "od-1xf__dPbK",
+    "outputId": "b39af2c4-c1ba-4d8a-c12d-12332c5bc1b6"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['y'].value_counts().plot(kind='bar', title='y')\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "22nkCfTbFiBB"
+   },
+   "source": [
+    "### Bonus 2 - Plot features using a foor loop\n",
+    "\n",
+    "First we must separete the column into lists of their types:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "id": "UrsF_S-9BtHp"
+   },
+   "outputs": [],
+   "source": [
+    "categorical_columns = ['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'poutcome']\n",
+    "numerical_columns = ['age', 'balance', 'day', 'duration', 'campaign', 'pdays', 'previous']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "7_MO8DPiN6Iz"
+   },
+   "source": [
+    "#### Categorical plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 873
+    },
+    "id": "2U9HsQqUAmd0",
+    "outputId": "da641a0c-0d0d-4c17-dd11-a36ce3d75137"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x1200 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n_cols = 3  # define the number of columns\n",
+    "\n",
+    "# now we check if the number of columns of that type is divisible by the number\n",
+    "# of columns we defined\n",
+    "if len(categorical_columns) % n_cols == 0:  # if it is devisible (no remainder)\n",
+    "    n_rows = int(len(categorical_columns) / n_cols)\n",
+    "else:  # if it is not devisible (no remainder)\n",
+    "    n_rows = int(len(categorical_columns) / n_cols) + 1\n",
+    "\n",
+    "# define the figure and its subplots\n",
+    "fig, axes = plt.subplots(nrows=n_rows,ncols=n_cols, figsize=(18, 12))\n",
+    "\n",
+    "cur_col = 0 # here we'll store the current column of the plot we are at\n",
+    "cur_row = 0 # here we'll store the current row of the plot we are at\n",
+    "\n",
+    "for column_name in categorical_columns:  # iterate over the column's names\n",
+    "    # define the plot passing current row and column to the axes param of the plot\n",
+    "    df[column_name].value_counts().plot(kind='bar', ax=axes[cur_row, cur_col], title=column_name)\n",
+    "    # once we add a new plot, we must move one column to the right, so we add 1 to\n",
+    "    # the current count of columns\n",
+    "    cur_col += 1\n",
+    "    # after, we must check we are still within the boundaries (remember that python\n",
+    "    # start indexing at 0)\n",
+    "    if cur_col == n_cols: # if we are beyond the boundary:\n",
+    "        cur_row += 1  # move to the next row (i.e., increment the row count)\n",
+    "        cur_col = 0 # reset the count of columns (i.e., position at the first column)\n",
+    "\n",
+    "# remove unused axes\n",
+    "for ax in axes.flat[len(numerical_columns):]:\n",
+    "    ax.remove()        \n",
+    "\n",
+    "# clean layout   \n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "f1tkGZXZN_Nk"
+   },
+   "source": [
+    "#### Numerical plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 873
+    },
+    "id": "dHZqhsQJBfpo",
+    "outputId": "94834c97-0d6c-4787-ffe2-2a1da90dc365"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x1200 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n_cols = 3  # define the number of columns\n",
+    "\n",
+    "# now we check if the number of columns of that type is divisible by the number\n",
+    "# of columns we defined\n",
+    "if len(numerical_columns) % n_cols == 0:  # if it is devisible (no remainder)\n",
+    "    n_rows = int(len(numerical_columns) / n_cols)\n",
+    "else:  # if it is not devisible (no remainder)\n",
+    "    n_rows = int(len(numerical_columns) / n_cols) + 1\n",
+    "\n",
+    "# define the figure and its subplots\n",
+    "fig, axes = plt.subplots(nrows=n_rows,ncols=n_cols, figsize=(18, 12))\n",
+    "\n",
+    "cur_col = 0 # here we'll store the current column of the plot we are at\n",
+    "cur_row = 0 # here we'll store the current row of the plot we are at\n",
+    "\n",
+    "for column_name in numerical_columns:  # iterate over the column's names\n",
+    "    # define the plot passing current row and column to the axes param of the plot\n",
+    "    df[column_name].plot(kind='hist', ax=axes[cur_row, cur_col], title=column_name)\n",
+    "    # once we add a new plot, we must move one column to the right, so we add 1 to\n",
+    "    # the current count of columns\n",
+    "    cur_col += 1\n",
+    "    # after, we must check we are still within the boundaries (remember that python\n",
+    "    # start indexing at 0)\n",
+    "    if cur_col == n_cols: # if we are beyond the boundary:\n",
+    "        cur_row += 1  # move to the next row (i.e., increment the row count)\n",
+    "        cur_col = 0 # reset the count of columns (i.e., position at the first column)\n",
+    "\n",
+    "# remove unused axes\n",
+    "for ax in axes.flat[len(numerical_columns):]:\n",
+    "    ax.remove()       \n",
+    "\n",
+    "# clean layout     \n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "suaadSYhuGGF"
+   },
+   "source": [
+    "## Challenge\n",
+    "\n",
+    "Choose one of the two methods above (Bonus 1 or Bonus 2) and try to combine the plots for all features (continuous and categoricals) in a single plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "id": "vpFiQzzcuTM5"
+   },
+   "outputs": [],
+   "source": [
+    "# write your code here"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Lab02-PythonSolution",
+   "provenance": []
+  },
+  "interpreter": {
+   "hash": "f2fb7fda8d2754165ee53020f70d8739bfae2298bc6c1a05d385a37b77f23af3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}